Universal quantum fluctuations of a cavity mode driven by a Josephson junction

We analyze the quantum dynamics of a superconducting cavity coupled to a voltage biased Josephson junction. The cavity is strongly excited at resonances where the voltage energy lost by a Cooper pair traversing the circuit is a multiple of the cavity photon energy. We find that the resonances are accompanied by substantial squeezing of the quantum fluctuations of the cavity over a broad range of parameters and are able to identify regimes where the fluctuations in the system take on universal values.Comment: 5 pages, 4 figure

Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines

Introduction. Nowadays, fault diagnosis of induction machines plays an important role in industrial fields. In this paper, Artificial Neural Network (ANN) model has been proposed for automatic fault diagnosis of an induction machine. The aim of this research study is to design a neural network model that allows generating a large database. This database can cover maximum possible of the stator faults. The fault considered in this study take into account a short circuit with large variations in the machine load. Moreover, the objective is to automate the diagnosis algorithm by using ANN classifier. Method. The database used for the ANN is based on indicators which are obtained from wavelet analysis of the machine stator current of one phase. The developed neural model allows to taking in consideration imbalances which are generated by short circuits in the machine stator. The implemented mathematical model in the expert system is based on a three-phase model. The mathematical parameters considered in this model are calculated online. The characteristic vector of the ANN model is formed by decomposition of stator current signal using wavelet discrete technique. Obtained results show that this technique allows to ensure more detection with clear evaluation of turn number in short circuit. Also, the developed expert system for the taken configurations is characterized by high precision.Вступ. Нині діагностика несправностей асинхронних машин відіграє значну роль у промисловості. У цій статті запропоновано модель штучної нейронної мережі для автоматичної діагностики несправностей асинхронної машини. Метою цього дослідження є розробка моделі нейронної мережі, що дозволяє генерувати велику базу даних. Ця база може охоплювати максимально можливі несправності статора. Несправності, розглянуті у цьому дослідженні, враховують коротке замикання при великих коливаннях навантаження машини. Крім того, мета полягає в тому, щоб автоматизувати алгоритм діагностики за допомогою класифікатора штучної нейронної мережі. Метод. База даних, що використовується для штучної нейронної мережі, заснована на показниках, отриманих в результаті вейвлет-аналізу струму статора машини однієї фази. Розроблена нейронна модель дозволяє враховувати дисбаланси, що виникають при коротких замиканнях у статорі машини. Реалізована математична модель в експертній системі ґрунтується на трифазній моделі. Математичні параметри, що враховуються в цій моделі, розраховуються онлайн. Характеристичний вектор моделі штучної нейронної мережі формується шляхом розкладання сигналу струму статора з використанням вейвлет-дискретного методу. Отримані результати показують, що дана методика дозволяє забезпечити більше виявлення з чіткою оцінкою числа витків при короткому замиканні. Також розроблена експертна система для конфігурацій, що приймаються, відрізняється високою точністю

Artificial intelligence to predict inhibition performance of pitting corrosion

This work aims to compare several algorithms for predicting the inhibition performance of localized corrosion. For this more than 400 electrochemical experiments were carried out in a corrosive solution containing an inorganic inhibitor. Pitting potential is used to indicate the performance of the inhibitor/oxidant mixture to prevent pitting corrosion. At the end of the electrochemical program a file containing all the experimental results has been prepared and submitted to several algorithms. Through a training phase each algorithm uses a set of experimental results to adjust its parameters and another set to predict the pitting potential starting from the properties and the chemical composition of the solution. The prediction performance of an algorithm is estimated by the  difference between experimental pitting potential and the calculated one. The order of performance of the algorithms is: GA-ANN > LS-SVM > PSO-ANN > ANN >ANFIS > KNN > RT > KBP > LDA.Key words: Pitting potential, Corrosion inhibitor, Performance prediction, Artificial intelligence

Etude de l’influence de l’état de surface sur la tenue à la fatigue d’un acier XC48

On parle communément de la durabilité d'un matériau comme s'il s'agissait d'une propriété fondamentale définie comme la période de temps pendant laquelle le matériau accomplira une fonction utile. La vie utile d'un matériau en service dépend cependant toujours non seulement de la combinaison des facteurs de charge et d'environnement auxquels il est soumis mais aussi des facteurs d'intégrité dont l'état de surface constitue un facteur très important, et c'est ainsi qu'il faut toujours tenir compte des paramètres d'état des surfaces dès la mise en service lorsque l'on considère la durabilité, ou vie du matériau en service. Cette dépendance est encore plus ressentie dans le cas des matériaux utilisés dans la confection de composants sollicités à la fatigue. Que ce soit en fatigue mécanique ou en fatigue de contact, les gradients de propriété au voisinage de la surface (microstructure, écrouissage, contraintes résiduelles) et les imperfections superficielles apportées par les procèdes de fabrication jouent un rôle primordial sur la tenue à la fatigue en service du composant. La présente étude vise à établir l'influence des paramètres de surfaces réalisées par tournage sur la résistance à la fatigue d'un acier XC 48, en s'attachant plus particulièrement a identifier l'effet de la rugosité qui peut être produite sous différentes conditions d'usinage des surfaces. Pour ce faire, diverses éprouvettes, présentant des combinaisons variées d'état de surfaces, réalisées par tournage à différents paramètres de coupe ont été soumises à des essais de flexion rotative et l'effet sur la durée de vie et la limite de fatigue, pour divers conditions de chargement, a été évalu

Investigation of Parietal Polysaccharides from Retama raetam Roots

This study characterizes the cell wall hemicellulose and pectins polymers of Retama raetam. This species develops a particularly important root system and is adapted to arid areas. The cellulose, hemicelluloses and pectins were extracted. The cellulose remains the major component of the wall (27% for young roots and 80% for  adult roots), hemicelluloses (14.3% for young roots and 3.6%  for adult roots) and pectins (17.3% for young roots and 4.1% for adult roots). The monosaccharidic composition of water soluble extracts determined by gas liquid chromatography (GLC) and completed by infrared (FTIR) spectroscopy of hemicellulosic shows the presence of xylose as a major monosaccharide in the non-cellulose polysaccharides (47.8% for young roots and 59.5% for adult roots). These results indicate the presence of the homogalacturonans and rhamnogalacturonans in pectin. This study constitutes the preliminary data obtained in the biochemical analysis of the parietal compounds of the roots of a species which grows in an arid area in comparison with those of its aerial parts.Keywords: Retama raetam, Roots, Cell Wall, Investigation, Polysaccharides, Monosaccharidi

Proposal for an ecofriendly and economic strategy for efficient radioiodination of coumarin derivatives

Combination of the calculation of reactivity descriptors and the cold iodine test for some coumarin derivatives was used in order to optimize the radioiodination reaction. The strongly nucleophilic predicted coumarins were subjected to the action of cold iodine. With two coumarins substituted at 3 by the 2-hydroxybenzoyl group, iodination did not occur but a product of intramolecular heterocyclization was obtained. This strategy is useful for economic and environmentally friendly radioiodination.publishe

Agri-Food Wastes for Bioplastics: European Prospective on Possible Applications in Their Second Life for a Circular Economy

Agri-food wastes (such as brewer’s spent grain, olive pomace, residual pulp from fruit juice production, etc.) are produced annually in very high quantities posing a serious problem, both environmentally and economically. These wastes can be used as secondary starting materials to produce value-added goods within the principles of the circular economy. In this context, this review focuses on the use of agri-food wastes either to produce building blocks for bioplastics manufacturing or biofillers to be mixed with other bioplastics. The pros and cons of the literature analysis have been highlighted, together with the main aspects related to the production of bioplastics, their use and recycling. The high number of European Union (EU)-funded projects for the valorisation of agri-food waste with the best European practices for this industrial sector confirm a growing interest in safeguarding our planet from environmental pollution. However, problems such as the correct labelling and separation of bioplastics from fossil ones remain open and to be optimised, with the possibility of reuse before final composting and selective recovery of biomass

Aerodynamic analysis models for vertical-axis wind turbines

This work details the progress made in the development of aerodynamic models for studying Vertical-Axis Wind Turbines (VAWT’ s) with particular emphasis on the prediction of aerodynamic loads and rotor performance as well as dynamic stall simulations. The paper describes current effort and some important findings using streamtube models, 3-D viscous model, stochastic wind model and numerical simulation of the flow around the turbine blades. Comparison of the analytical results with available experimental data have shown good agreement

OPTIMIZATION OF LEARNING ALGORITHMS IN THE PREDICTION OF PITTING CORROSION

This work is part of a scientific research program whose objective is to prevent pitting corrosion of an open cooling circuit of a nuclear installation. Various corrosion inhibitors have been studied. The performances of pitting corrosion inhibition were discussed and compared on the basis of several criteria. The experimental data were collected in a large table and subjected to algorithms in order to construct models for predicting corrosion inhibition performance. We used four algorithms: Genetic Algorithm-Artificial Neural Network (GAANN); Least Squares-Support Vector Machine (LS-SVM), K Nearest Neighbors (KNN) and Regression Tree (RT). We optimized the data fraction reserved for learning and we sought to optimize the parameters specific to each algorithm. The efficiency of pitting inhibition increases in the following order: HCO3- < H2PO4- < CO32- < PO4-2 < PO4 3- < SiO3 2- < MoO4 2- < WO4 2-. Our results showed that the order of performance of the algorithms is: RT < KNN < LS-SVM < GA-ANN